Optimize with BayesOpt¶

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Installation¶

In [ ]:

!pip install bayesian-optimization

!pip install bayesian-optimization

Sample Strategy¶

In [1]:

# import talib.abstract as ta

from lettrade import DataFeed, Strategy, indicator as i
from lettrade.exchange.backtest import ForexBackTestAccount, let_backtest
from lettrade.indicator.vendor.qtpylib import inject_indicators

inject_indicators()


class SmaCross(Strategy):
    ema1_period = 9
    ema2_period = 21

    def indicators(self, df: DataFeed):
        # df["ema1"] = ta.EMA(df, timeperiod=self.ema1_period)
        # df["ema2"] = ta.EMA(df, timeperiod=self.ema2_period)
        df["ema1"] = df.close.ema(window=self.ema1_period)
        df["ema2"] = df.close.ema(window=self.ema2_period)

        df["signal_ema_crossover"] = i.crossover(df.ema1, df.ema2)
        df["signal_ema_crossunder"] = i.crossunder(df.ema1, df.ema2)

    def next(self, df: DataFeed):
        if len(self.orders) > 0 or len(self.positions) > 0:
            return

        if df.l.signal_ema_crossover[-1]:
            price = df.l.close[-1]
            self.buy(size=0.1, sl=price - 0.001, tp=price + 0.001)
        elif df.l.signal_ema_crossunder[-1]:
            price = df.l.close[-1]
            self.sell(size=0.1, sl=price + 0.001, tp=price - 0.001)


lt = let_backtest(
    strategy=SmaCross,
    datas="example/data/data/EURUSD_5m-0_10000.csv",
    account=ForexBackTestAccount,
    # plotter=None,
)

# import talib.abstract as ta from lettrade import DataFeed, Strategy, indicator as i from lettrade.exchange.backtest import ForexBackTestAccount, let_backtest from lettrade.indicator.vendor.qtpylib import inject_indicators inject_indicators() class SmaCross(Strategy): ema1_period = 9 ema2_period = 21 def indicators(self, df: DataFeed): # df["ema1"] = ta.EMA(df, timeperiod=self.ema1_period) # df["ema2"] = ta.EMA(df, timeperiod=self.ema2_period) df["ema1"] = df.close.ema(window=self.ema1_period) df["ema2"] = df.close.ema(window=self.ema2_period) df["signal_ema_crossover"] = i.crossover(df.ema1, df.ema2) df["signal_ema_crossunder"] = i.crossunder(df.ema1, df.ema2) def next(self, df: DataFeed): if len(self.orders) > 0 or len(self.positions) > 0: return if df.l.signal_ema_crossover[-1]: price = df.l.close[-1] self.buy(size=0.1, sl=price - 0.001, tp=price + 0.001) elif df.l.signal_ema_crossunder[-1]: price = df.l.close[-1] self.sell(size=0.1, sl=price + 0.001, tp=price - 0.001) lt = let_backtest( strategy=SmaCross, datas="example/data/data/EURUSD_5m-0_10000.csv", account=ForexBackTestAccount, # plotter=None, )

Optimize¶

In [3]:

from bayes_opt import BayesianOptimization

lettrade_model = lt.optimize_model()


def train_model(**params):
    params = {
        "ema1_period": int(params["ema1_period"]),
        "ema2_period": int(params["ema2_period"]),
    }
    # Model
    result = lettrade_model(params)

    # Score
    return result["equity"]


pbounds = {"ema1_period": (5, 25), "ema2_period": (10, 50)}

optimizer = BayesianOptimization(
    f=train_model,
    pbounds=pbounds,
    random_state=1,
)

optimizer.maximize(
    init_points=2,
    n_iter=200,
)

from bayes_opt import BayesianOptimization lettrade_model = lt.optimize_model() def train_model(**params): params = { "ema1_period": int(params["ema1_period"]), "ema2_period": int(params["ema2_period"]), } # Model result = lettrade_model(params) # Score return result["equity"] pbounds = {"ema1_period": (5, 25), "ema2_period": (10, 50)} optimizer = BayesianOptimization( f=train_model, pbounds=pbounds, random_state=1, ) optimizer.maximize( init_points=2, n_iter=200, )

|   iter    |  target   | ema1_p... | ema2_p... |
-------------------------------------------------
| 1         | 922.0     | 13.34     | 38.81     |
| 2         | 875.4     | 5.002     | 22.09     |
| 3         | 921.0     | 13.3      | 39.0      |
| 4         | 971.3     | 18.52     | 33.86     |
| 5         | 864.5     | 25.0      | 30.34     |
| 6         | 1.031e+03 | 15.36     | 31.85     |
| 7         | 1.05e+03  | 14.5      | 28.01     |
| 8         | 909.8     | 17.22     | 21.41     |
| 9         | 919.6     | 10.73     | 30.04     |
| 10        | 1.011e+03 | 17.12     | 28.92     |
| 11        | 989.6     | 13.57     | 25.4      |
| 12        | 1.01e+03  | 25.0      | 50.0      |
| 13        | 1e+03     | 20.13     | 50.0      |
| 14        | 1.008e+03 | 23.5      | 45.25     |
| 15        | 971.5     | 13.03     | 49.99     |
| 16        | 1.118e+03 | 24.91     | 10.16     |
| 17        | 1.008e+03 | 21.38     | 10.4      |
| 18        | 1.169e+03 | 24.99     | 12.98     |
| 19        | 990.6     | 25.0      | 16.16     |
| 20        | 1.158e+03 | 23.87     | 12.29     |
| 21        | 1.148e+03 | 24.79     | 11.96     |
| 22        | 1.159e+03 | 22.27     | 14.03     |
| 23        | 1.117e+03 | 18.79     | 14.72     |
| 24        | 1.059e+03 | 14.08     | 13.05     |
| 25        | 923.6     | 8.15      | 10.0      |
| 26        | 905.5     | 5.0       | 50.0      |
| 27        | 931.9     | 5.0       | 38.91     |
| 28        | 1.001e+03 | 25.0      | 39.59     |
| 29        | 1.139e+03 | 20.65     | 16.19     |
| 30        | 980.7     | 12.11     | 16.74     |
| 31        | 1.12e+03  | 15.66     | 10.0      |
| 32        | 1.059e+03 | 17.29     | 11.85     |
| 33        | 1.154e+03 | 13.36     | 10.19     |
| 34        | 958.8     | 18.21     | 44.02     |
| 35        | 1.169e+03 | 23.68     | 13.74     |
| 36        | 1.148e+03 | 20.63     | 14.68     |
| 37        | 1.169e+03 | 23.13     | 13.15     |
| 38        | 1.129e+03 | 24.16     | 13.11     |
| 39        | 1.168e+03 | 18.55     | 17.01     |
| 40        | 1e+03     | 16.67     | 16.31     |
| 41        | 1.1e+03   | 19.84     | 18.24     |
| 42        | 1e+03     | 11.9      | 11.48     |
| 43        | 1.184e+03 | 14.49     | 10.92     |
| 44        | 1.159e+03 | 25.0      | 22.99     |
| 45        | 941.1     | 22.97     | 23.43     |
| 46        | 1.049e+03 | 25.0      | 21.12     |
| 47        | 1e+03     | 24.94     | 24.76     |
| 48        | 1.08e+03  | 22.61     | 15.52     |
| 49        | 1.168e+03 | 19.31     | 16.32     |
| 50        | 832.2     | 5.0       | 31.88     |
| 51        | 990.6     | 9.272     | 44.54     |
| 52        | 940.6     | 5.0       | 15.35     |
| 53        | 903.3     | 20.28     | 38.8      |
| 54        | 1.117e+03 | 20.74     | 12.74     |
| 55        | 851.4     | 5.0       | 44.28     |
| 56        | 991.8     | 10.69     | 21.4      |
| 57        | 983.1     | 13.37     | 45.28     |
| 58        | 1.128e+03 | 23.3      | 11.13     |
| 59        | 920.4     | 25.0      | 35.58     |
| 60        | 889.9     | 9.795     | 35.56     |
| 61        | 951.9     | 8.697     | 25.63     |
| 62        | 1.184e+03 | 14.27     | 10.07     |
| 63        | 989.0     | 16.82     | 48.69     |
| 64        | 966.4     | 9.206     | 49.78     |
| 65        | 991.4     | 25.0      | 42.71     |
| 66        | 1.09e+03  | 24.98     | 14.45     |
| 67        | 805.2     | 8.466     | 17.84     |
| 68        | 1.02e+03  | 18.29     | 25.92     |
| 69        | 941.2     | 20.6      | 29.15     |
| 70        | 901.6     | 5.0       | 10.0      |
| 71        | 961.5     | 14.86     | 34.82     |
| 72        | 931.4     | 20.66     | 46.99     |
| 73        | 1.08e+03  | 15.34     | 11.78     |
| 74        | 1e+03     | 18.08     | 18.34     |
| 75        | 1.01e+03  | 22.07     | 18.61     |
| 76        | 880.2     | 9.328     | 40.89     |
| 77        | 1.049e+03 | 18.56     | 10.05     |
| 78        | 882.6     | 5.0       | 26.96     |
| 79        | 1.164e+03 | 13.78     | 11.11     |
| 80        | 1.129e+03 | 19.37     | 17.16     |
| 81        | 1.168e+03 | 18.14     | 16.05     |
| 82        | 1.016e+03 | 25.0      | 47.21     |
| 83        | 1.168e+03 | 22.34     | 12.15     |
| 84        | 974.0     | 16.34     | 41.14     |
| 85        | 821.7     | 13.49     | 21.54     |
| 86        | 918.2     | 21.74     | 42.48     |
| 87        | 1.158e+03 | 21.96     | 13.07     |
| 88        | 923.7     | 10.85     | 46.89     |
| 89        | 1.101e+03 | 23.06     | 14.07     |
| 90        | 1.006e+03 | 19.01     | 12.76     |
| 91        | 1.149e+03 | 21.56     | 14.85     |
| 92        | 923.2     | 21.72     | 33.55     |
| 93        | 1.158e+03 | 21.29     | 13.86     |
| 94        | 1e+03     | 20.49     | 20.64     |
| 95        | 1.01e+03  | 13.94     | 30.14     |
| 96        | 1.009e+03 | 16.0      | 26.27     |
| 97        | 841.1     | 9.332     | 13.73     |
| 98        | 985.5     | 17.17     | 37.25     |
| 99        | 1.01e+03  | 22.63     | 50.0      |
| 100       | 880.7     | 5.143     | 35.8      |
| 101       | 913.7     | 13.63     | 42.64     |
| 102       | 1.168e+03 | 19.58     | 15.44     |
| 103       | 932.0     | 15.93     | 46.05     |
| 104       | 840.4     | 11.58     | 26.91     |
| 105       | 1.01e+03  | 12.79     | 32.74     |
| 106       | 1.158e+03 | 23.05     | 12.15     |
| 107       | 961.2     | 17.78     | 31.3      |
| 108       | 1.023e+03 | 8.636     | 22.57     |
| 109       | 941.2     | 20.78     | 26.03     |
| 110       | 1.166e+03 | 18.79     | 15.91     |
| 111       | 1.007e+03 | 16.62     | 13.95     |
| 112       | 1.184e+03 | 14.1      | 10.57     |
| 113       | 940.8     | 7.458     | 47.12     |
| 114       | 1.184e+03 | 14.89     | 10.32     |
| 115       | 999.5     | 18.99     | 23.66     |
| 116       | 949.5     | 24.97     | 18.99     |
| 117       | 1.049e+03 | 24.5      | 22.33     |
| 118       | 1.116e+03 | 23.73     | 10.0      |
| 119       | 1.168e+03 | 18.5      | 16.46     |
| 120       | 911.5     | 8.835     | 32.3      |
| 121       | 880.0     | 5.046     | 18.56     |
| 122       | 1.148e+03 | 24.21     | 11.24     |
| 123       | 962.2     | 15.41     | 50.0      |
| 124       | 1.097e+03 | 21.39     | 11.92     |
| 125       | 1.178e+03 | 11.87     | 10.0      |
| 126       | 1e+03     | 10.56     | 10.18     |
| 127       | 1.139e+03 | 12.7      | 10.04     |
| 128       | 990.6     | 13.53     | 15.0      |
| 129       | 1.08e+03  | 21.78     | 16.77     |
| 130       | 947.8     | 23.09     | 37.81     |
| 131       | 863.8     | 10.61     | 23.71     |
| 132       | 959.9     | 16.52     | 24.21     |
| 133       | 1.148e+03 | 20.36     | 15.5      |
| 134       | 1.011e+03 | 5.0       | 12.84     |
| 135       | 1.184e+03 | 14.74     | 10.0      |
| 136       | 1.184e+03 | 14.52     | 10.43     |
| 137       | 960.3     | 7.708     | 28.99     |
| 138       | 1.166e+03 | 17.95     | 16.78     |
| 139       | 940.7     | 18.11     | 50.0      |
| 140       | 1.017e+03 | 25.0      | 45.27     |
| 141       | 1.168e+03 | 22.49     | 12.67     |
| 142       | 961.8     | 19.81     | 35.99     |
| 143       | 879.5     | 8.582     | 20.84     |
| 144       | 1.033e+03 | 7.081     | 24.03     |
| 145       | 996.9     | 23.18     | 47.91     |
| 146       | 1.127e+03 | 20.13     | 13.88     |
| 147       | 1.17e+03  | 14.2      | 11.49     |
| 148       | 923.5     | 18.7      | 41.33     |
| 149       | 1.049e+03 | 25.0      | 23.51     |
| 150       | 981.4     | 24.98     | 27.02     |
| 151       | 973.7     | 13.96     | 18.19     |
| 152       | 1.08e+03  | 20.63     | 17.45     |
| 153       | 970.6     | 15.55     | 29.6      |
| 154       | 961.4     | 14.02     | 47.71     |
| 155       | 1.049e+03 | 24.98     | 22.57     |
| 156       | 959.1     | 23.3      | 20.83     |
| 157       | 881.3     | 7.992     | 38.11     |
| 158       | 1.08e+03  | 17.09     | 10.3      |
| 159       | 922.5     | 5.0       | 41.39     |
| 160       | 970.8     | 12.59     | 35.8      |
| 161       | 960.8     | 18.53     | 27.76     |
| 162       | 941.3     | 11.17     | 43.54     |
| 163       | 1.119e+03 | 17.77     | 15.05     |
| 164       | 940.8     | 22.92     | 27.71     |
| 165       | 1.012e+03 | 11.51     | 19.26     |
| 166       | 884.8     | 24.8      | 33.0      |
| 167       | 971.5     | 15.79     | 38.79     |
| 168       | 911.3     | 5.035     | 47.45     |
| 169       | 937.9     | 23.42     | 40.72     |
| 170       | 961.7     | 16.58     | 33.52     |
| 171       | 1.148e+03 | 24.98     | 11.13     |
| 172       | 1.129e+03 | 24.97     | 13.62     |
| 173       | 941.5     | 20.15     | 31.63     |
| 174       | 928.6     | 18.3      | 46.82     |
| 175       | 1.169e+03 | 21.41     | 15.7      |
| 176       | 891.0     | 7.671     | 42.85     |
| 177       | 900.6     | 6.871     | 12.27     |
| 178       | 1.15e+03  | 13.45     | 12.11     |
| 179       | 849.7     | 12.57     | 13.27     |
| 180       | 852.6     | 5.0       | 24.54     |
| 181       | 949.0     | 21.04     | 44.71     |
| 182       | 1.09e+03  | 24.07     | 14.53     |
| 183       | 1.141e+03 | 16.05     | 10.99     |
| 184       | 950.0     | 10.93     | 38.37     |
| 185       | 1.12e+03  | 15.24     | 10.87     |
| 186       | 952.7     | 11.13     | 50.0      |
| 187       | 1.05e+03  | 20.6      | 22.67     |
| 188       | 913.5     | 16.03     | 43.42     |
| 189       | 987.5     | 19.71     | 11.13     |
| 190       | 1.166e+03 | 19.73     | 14.75     |
| 191       | 969.9     | 11.15     | 33.38     |
| 192       | 1.097e+03 | 22.32     | 11.42     |
| 193       | 1.06e+03  | 14.14     | 12.22     |
| 194       | 1.007e+03 | 15.01     | 14.48     |
| 195       | 974.5     | 7.309     | 49.96     |
| 196       | 1.164e+03 | 13.04     | 11.62     |
| 197       | 813.7     | 15.88     | 18.95     |
| 198       | 1e+03     | 19.4      | 19.51     |
| 199       | 1.139e+03 | 12.18     | 10.46     |
| 200       | 971.1     | 15.01     | 36.88     |
| 201       | 1.128e+03 | 24.51     | 23.1      |
| 202       | 990.2     | 16.04     | 27.8      |
=================================================

In [4]:

optimizer.max

optimizer.max

Out[4]:

{'target': 1183.88,
 'params': {'ema1_period': 14.491292925643293,
  'ema2_period': 10.915994213005602}}

Plot¶

In [5]:

lt.plotter.heatmap(x="ema1_period", y="ema2_period", z="equity")

lt.plotter.heatmap(x="ema1_period", y="ema2_period", z="equity")

In [6]:

lt.plotter.contour(x="ema1_period", y="ema2_period", z="equity")

lt.plotter.contour(x="ema1_period", y="ema2_period", z="equity")

Plot plotly¶

Init Plotly environment¶

In [7]:

import plotly.io as pio

pio.renderers.default = "notebook"
pio.templates.default = "plotly_dark"

import plotly.io as pio pio.renderers.default = "notebook" pio.templates.default = "plotly_dark"

In [8]:

optimizer.res

optimizer.res

Out[8]:

[{'target': 921.98,
  'params': {'ema1_period': 13.34044009405148,
   'ema2_period': 38.81297973768632}},
 {'target': 875.38,
  'params': {'ema1_period': 5.002287496346898,
   'ema2_period': 22.09330290527359}},
 {'target': 920.98,
  'params': {'ema1_period': 13.3001407276959,
   'ema2_period': 39.00039537269421}},
 {'target': 971.28,
  'params': {'ema1_period': 18.52065437272396,
   'ema2_period': 33.863457550993026}},
 {'target': 864.48,
  'params': {'ema1_period': 25.0, 'ema2_period': 30.33928758479602}},
 {'target': 1031.18,
  'params': {'ema1_period': 15.357337759328532,
   'ema2_period': 31.848048898351127}},
 {'target': 1049.68,
  'params': {'ema1_period': 14.50054591701167,
   'ema2_period': 28.014062012896545}},
 {'target': 909.78,
  'params': {'ema1_period': 17.22182187422121,
   'ema2_period': 21.409928335006267}},
 {'target': 919.58,
  'params': {'ema1_period': 10.73144001218241,
   'ema2_period': 30.04066974540485}},
 {'target': 1010.88,
  'params': {'ema1_period': 17.115149925746156,
   'ema2_period': 28.917781696081047}},
 {'target': 989.58,
  'params': {'ema1_period': 13.570348096696343,
   'ema2_period': 25.404096691580644}},
 {'target': 1010.28, 'params': {'ema1_period': 25.0, 'ema2_period': 50.0}},
 {'target': 1000.18,
  'params': {'ema1_period': 20.13399772503066, 'ema2_period': 50.0}},
 {'target': 1008.28,
  'params': {'ema1_period': 23.495361736035818,
   'ema2_period': 45.25289027522756}},
 {'target': 971.48,
  'params': {'ema1_period': 13.029692423735854,
   'ema2_period': 49.99334297246561}},
 {'target': 1118.38,
  'params': {'ema1_period': 24.906030330322487,
   'ema2_period': 10.157182041157764}},
 {'target': 1007.98,
  'params': {'ema1_period': 21.37923376276011,
   'ema2_period': 10.395845348298236}},
 {'target': 1168.98,
  'params': {'ema1_period': 24.99158812480974,
   'ema2_period': 12.98307192822218}},
 {'target': 990.58,
  'params': {'ema1_period': 25.0, 'ema2_period': 16.160763482984994}},
 {'target': 1158.38,
  'params': {'ema1_period': 23.86634900198165,
   'ema2_period': 12.294075748068922}},
 {'target': 1148.18,
  'params': {'ema1_period': 24.790930666853647,
   'ema2_period': 11.963961085904518}},
 {'target': 1158.88,
  'params': {'ema1_period': 22.265459234415193,
   'ema2_period': 14.030698582520076}},
 {'target': 1116.68,
  'params': {'ema1_period': 18.787132211610984,
   'ema2_period': 14.716136499573038}},
 {'target': 1059.38,
  'params': {'ema1_period': 14.075693848032216,
   'ema2_period': 13.051766130408804}},
 {'target': 923.58,
  'params': {'ema1_period': 8.149890181902189, 'ema2_period': 10.0}},
 {'target': 905.48, 'params': {'ema1_period': 5.0, 'ema2_period': 50.0}},
 {'target': 931.88,
  'params': {'ema1_period': 5.0, 'ema2_period': 38.90563746761259}},
 {'target': 1001.38,
  'params': {'ema1_period': 25.0, 'ema2_period': 39.5881538636381}},
 {'target': 1139.08,
  'params': {'ema1_period': 20.651108713610626,
   'ema2_period': 16.189379061835858}},
 {'target': 980.68,
  'params': {'ema1_period': 12.111792096067155,
   'ema2_period': 16.738731828066545}},
 {'target': 1120.48,
  'params': {'ema1_period': 15.659039064119396, 'ema2_period': 10.0}},
 {'target': 1059.08,
  'params': {'ema1_period': 17.294894350267416,
   'ema2_period': 11.854171255613114}},
 {'target': 1153.78,
  'params': {'ema1_period': 13.358213084249549,
   'ema2_period': 10.194642176893609}},
 {'target': 958.78,
  'params': {'ema1_period': 18.205943303763114,
   'ema2_period': 44.0207020913403}},
 {'target': 1168.98,
  'params': {'ema1_period': 23.680446326785688,
   'ema2_period': 13.735838545453007}},
 {'target': 1148.28,
  'params': {'ema1_period': 20.62613055844256,
   'ema2_period': 14.675593400394167}},
 {'target': 1168.98,
  'params': {'ema1_period': 23.125098159210452,
   'ema2_period': 13.152688839392106}},
 {'target': 1129.28,
  'params': {'ema1_period': 24.15750803502253,
   'ema2_period': 13.106871154420155}},
 {'target': 1167.88,
  'params': {'ema1_period': 18.55024818513378,
   'ema2_period': 17.011813859705974}},
 {'target': 1000.0,
  'params': {'ema1_period': 16.666731907272982,
   'ema2_period': 16.309972709233975}},
 {'target': 1099.88,
  'params': {'ema1_period': 19.84295688106342,
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 {'target': 990.18,
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   'ema2_period': 27.79675829919071}}]

In [9]:

import pandas as pd

df = pd.DataFrame(columns=["ema1_period", "ema2_period", "score"])
for i, trial in enumerate(optimizer.res):
    df.loc[i] = [
        int(trial["params"]["ema1_period"]),
        int(trial["params"]["ema2_period"]),
        trial["target"],
    ]

df

import pandas as pd df = pd.DataFrame(columns=["ema1_period", "ema2_period", "score"]) for i, trial in enumerate(optimizer.res): df.loc[i] = [ int(trial["params"]["ema1_period"]), int(trial["params"]["ema2_period"]), trial["target"], ] df

Out[9]:

	ema1_period	ema2_period	score
0	13.0	38.0	921.98
1	5.0	22.0	875.38
2	13.0	39.0	920.98
3	18.0	33.0	971.28
4	25.0	30.0	864.48
...	...	...	...
197	19.0	19.0	1000.00
198	12.0	10.0	1139.08
199	15.0	36.0	971.08
200	24.0	23.0	1128.28
201	16.0	27.0	990.18

202 rows × 3 columns

Type 1¶

In [10]:

from plotly import express as px

fig = px.scatter(df, x=df.index, y="score")
fig.show()

from plotly import express as px fig = px.scatter(df, x=df.index, y="score") fig.show()

Type 2¶

In [11]:

import plotly.express as px


fig = px.density_contour(
    df,
    x="ema1_period",
    y="ema2_period",
    z="score",
    histfunc="max",
)
fig.update_traces(contours_coloring="fill", contours_showlabels=True)
fig.show()

import plotly.express as px fig = px.density_contour( df, x="ema1_period", y="ema2_period", z="score", histfunc="max", ) fig.update_traces(contours_coloring="fill", contours_showlabels=True) fig.show()

Type 3¶

In [12]:

import plotly.express as px

fig = px.density_heatmap(
    df,
    x="ema1_period",
    y="ema2_period",
    z="score",
    nbinsx=20,
    nbinsy=40,
    histfunc="max",
    color_continuous_scale="Viridis",
)
fig.show()

import plotly.express as px fig = px.density_heatmap( df, x="ema1_period", y="ema2_period", z="score", nbinsx=20, nbinsy=40, histfunc="max", color_continuous_scale="Viridis", ) fig.show()